Extracting Attractive Local-Area Topics in Georeferenced Documents using a New Density-based Spatial Clustering Algorithm - Sakai, Tatsuhiro; Tamura, Keiichi; Kitakami, Hajime (IAENG international journal of computer science, 2014)
Along with the popularization of social media, huge numbers of georeferenced documents (which include location information) are being posted on social media sites via the Internet, allowing people to transmit and collect geographic information. Typically, such georeferenced documents are related not only to personal topics but also to local topics and events. Therefore, extracting "attractive" areas associated with local topics from georeferenced documents is currently one of the most important challenges in different application domains. In this paper, a novel spatial clustering algorithm for extracting "attractive" local-area topics in georeferenced documents, known as the ([epsilon], [sigma])-density-based spatial clustering algorithm, is proposed. We defined a new type of spatial cluster called an ([epsilon], [sigma])-density-based spatial cluster. The proposed density-based spatial clustering algorithm can recognize both semantically and spatially separated spatial clusters. Therefore, the proposed algorithm can extract "attractive" local-area topics as ([epsilon], [sigma])-density-based spatial clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site were used. The experimental results showed that the ([epsilon], [sigma])-density-based spatial clustering algorithm could extract "attractive" areas as the ([epsilon], [sigma])-density-based spatial clusters that were closely related to local topics.